Question:: why text_based_Depression [ python run.py train ] not working. giving usage message but still stuck.

[faridelya]

Hello @RicherMans Hope you are doing Excellent. i am try from few days but still stuck. I have set Enviroment for Text Base Depression where i found that [ allennlp==0.8.5 ] given version is not compatable with required [ torch==1.2.0 ]version so allennlp automatically uninstall it and install compatable version 1.1.0 . ok its fine and i found duplicate directorys and files in [ System ] Folder it also fine i just correctly setup the import tcc.py in models.py . i also setup relative Folders path for input and output in gensim.py. please look at this 1st picture. here we only got two complete bar line. Actually i am using 300 -304 transcrip files and merged to all_transcript.csv . i run gensym.py the above output is shown and two file in[ data_preprocess / feature / text / ] has created but the files are empty . is that ok with this output?. Now check this postion of directories in 2nd picture.

I try to place all the folder [labes, labels_processed, feature/text ] outside the data_preprocess Folder so when i was running the error rise that [TypeError: Parameter doc_words of infer_vector() must be a list of strings (not a single string).] . what you suggest me ? should i place all these folder in first place position like shown in 1st picture.

ok so i place every folder inside data_prepocess and run gensim.py i got two files in feature/text folder which in my case is empty may be this is due to not giving all transcript files or may be some issue.

in last kindly shown ma how we run [ python run.py train --model GRU ] that giving us the following output. i tried alternate [ python run.py train ] but still same result . please guide me throughly . and Thanks alot

[RicherMans]

Hey there, first of all, I haven't used that repo in years and also don't have access to the data anymore, so my recommendations are limited.

Actually i am using 300 -304 transcrip files and merged to all_transcript.csv . i run gensym.py the above output is shown and two file in[ data_preprocess / feature / text / ] has created but the files are empty . is that ok with this output?.

No its definitely wrong, it means that speakers are completely missing i.e., no labels are provided for a given input, which just means it can't be trained. Have you checked if all speakers in train_split_Depression_AVEC2017.csv are within the --transcriptdir directory ? Differently said, are values in the Participant_ID in the train_split_Depression_AVEC2017.csv also present within your passed --transcriptdir, i.e., 300_TRANSCRIPT.csv ?

Any output you provided after that is obviously not working, since the data has not been properly processed, so don't worry about that.

in last kindly shown ma how we run [ python run.py train --model GRU ] that giving us the following output.

Well it says that you should give it a config file (a yaml) as input, like the one in config/*yaml. For example python3 run.py train config/text_lstm_deep.yaml

[faridelya]

Hi @RicherMans Thanks for giving us answere. so i am now showig the label files and along with --300_TRANSCRIPT.csv files and also the merged file --all_transcript.csv ok here are some pictures.

1. 300_TRANSCRIPT.csv
2. 301_TRANSCRIPT.csv
3. all_transcripts.csv
4. train_split_Depression_AVEC2017.csv
5. dev_split_Depression_AVEC2017.csv

here are the files that i have.

1. lables files dev_split_Depression_AVEC2017.csv train_split_Depression_AVEC2017.csv
2. here are the Transcipt files

300_TRANSCRIPT.csv 301_TRANSCRIPT.csv 302_TRANSCRIPT.csv 303_TRANSCRIPT.csv 304_TRANSCRIPT.csv all_transcripts.csv

i found the Participant ID No. 303 , 304 in train_split_depression_AVEC2017.csv and one participant ID No. 302 in dev_split_Depression_AVEC2017.csv . so it mean that i have given 300 to 304 transcript files it should have taken those speaker that are in label files. any way check . have a look at. again Thanks alot for your cooperation.

[RicherMans]

I simply suggest to not use non-pretrained gensim embeddings. I guess I have never thought that one might want to train embeddings themselves and left that code broken, sorry.

But its easy to fix, in extract_text_gensim.py change line 73 from:

features = np.array([model.infer_vector(
                    paragraph.lower()) for paragraph in transcript_df.value.values])

to:

paragraphs = np.array(transcript_df['value'].str.lower().values)
features = model.infer_vector(paragraphs)

[faridelya]

got new error after putting the code in on line 78 in gensim.py assert(len(m.shape) == 2), "'m' has to be 2d matrix!" AssertionError: 'm' has to be 2d matrix!

[RicherMans]

Just reshape features to :

features = features.reshape(1, -1_

But once again, please use any other model or the pretrained KeyedVector. In the paper I don't think I paid a lot of attention to the non-pretrained data.

[faridelya]

sorry for saying that because i am kind of new should i use bert or elmo that exist in same directory . i mean that are pretrained ?

[RicherMans]

Any of these, you need to download first the corresponding models, which is also true from the gensim models.

I just included this gensim baseline with no intention of ever using it, since in the current time it makes little sense to not use a pretrained model.

[faridelya]

ok after downloading pretraned model we should use the the same files at labels and labels_processs folder to get feature extraction. is this the whole point of working flow?

[faridelya]

Error# KeyError: 1 in [run.py] as gensim.py work smootly after added reshape(1,-1) now when i run [ run.py ] it give me this error

[RicherMans]

ok after downloading pretraned model we should use the the same files at labels and labels_processs folder to get feature extraction. is this the whole point of working flow?

Jep it is just extracting for each word or sentence some features.

as gensim.py work smootly after added reshape(1,-1) now when i run [ run.py ] it give me this error

Seems like I used for sampling some static index that goes from 0 to N-1, for each sample. You might want to try and use the entire dataset.

Also please note that the (1,-1) reshape is just a stupid fix, it probably won't run since you usually need a single feature for each sentence or so. The current "non-pretrained" code just extracts a single feature for the entire spoken content by a speaker, which is likely far too abstract.